Corpus Selection

For this research project, I selected 7 Dutch and 7 American hip hop artists. Besides comparing the two genres, this enables us to look at the characteristics of each individual artist. For each artist Spotify has created a ‘This is …’ playlist with around 50 songs. I noticed a lot of collaboration between artists. This might result in songs appearing in multiple playlists, which might have a misleading effect. To be able to do a good comparison, I decided to select only the songs by the artist himself. I hope this gives a good representation of the style and properties of the artists. After making this selection, the playlists differed a lot in size, but I accepted this because the number of songs for the dutch and american genre were almost the same. This analysis is focussed on relatively new hip hop, with songs uploaded from 2017 till now.

Spotify Playlists: Nederhop

Spotify Playlists: American hip hop

First look at the data

Findings

These are the means and standard deviations for the numeric features. The featue values for American and Dutch hip hop are alike. It can be noticed that the standard deviations for the Dutch hip hop are smaller. This was surprising, because in the literature, Nederhop seemed to be a broader genre.

Russell’s circumplex model

Emotion studies in HCI-a new approach - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Russells-circumplex-model-of-affect_fig1_229021134 [accessed 19 Mar, 2019]

Findings

On the left we see the data points in an enery valence space. This space can be linked to different emotions. Above, Russell’s circumplex model is displayed. In this model, the vertical axis represents arousal, which for now we consider comparable with energy. The horizontal axis represents valence. The middle represents medium valence and medium arousal.

Most data appears in the top half. This makes sense, considering hip hop is more happy or angry than relaxed or sad. American hip hop appears in the sad section more often. When looking at artists individually, we don’t see any clear clusters.

Findings

To define the danceability of a song, Spotify considers tempo, rhythm stability, beat strength, and overall regularity. As hip hop is a huge dance culture, it is expected that the songs are often very danceable. This is visible in the data. I didn’t think acoustic hip hop was very plausible, so I expected to find some outliers. I don’t think we should consider “Grand Piano” by Nicki Minaj hip hop, but I do think “Lust For Life” by Drake is a great example that acoustic hip hop is possible.

Findings

In the previous page we looked at the danceability of songs. Although danceability is not a very straight forward feature, I want to look for other things that might indicate the dancebility of songs. I selected “Gemaakt voor dit” by Lil’ Kleine and “Signs” by Snoop Dogg, because they are extremely danceable according to Spotify. After listening to the songs, I must say I agree.

For each song, the left self-similarity matrix is based on timbre, and the right one is based on chroma. The features were summarised at bar level and the axes are in seconds.

In the matrices, we see a checkerboard structure. These blocks represent homogeneous regions in the songs. The diagonal lines, parallel to the main diagnal represent exact repetions in the music. Both songs show a lot of repitions, which makes sense. To make it easy to dance to a song, I think the song should be predictable and that can be accomplished by repetition. The chroma and timbre patterns for “Gemaakt voor dit” seem to fit nicely on top of each other, showing that homogeneous regions in chroma correspond to homogenious regions in timbre.

HUMBLE. by Kendrick Lamar

Findings

Notes

I randomly selected 30 dutch songs and 30 american songs to perform machine learing on. To the left, we see an hierarchical clustering for the data. In the clusters on the left we see mostly american songs and in the cluster on the right we see mostly dutch songs. In the middle it is mixed.

The heatmap shows the intensity in which the features contributed to the clustering. As the clusters are all in the blue region, it is hard to point out features that are most responsible. Instrumentalness was apparantly very important for the song ‘Heard Em Say’, assiging it to a cluster of its own.

Notes